Picture of smart phone in human hand

World leading smartphone and mobile technology research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by Strathclyde researchers from the Department of Computer & Information Sciences involved in researching exciting new applications for mobile and smartphone technology. But the transformative application of mobile technologies is also the focus of research within disciplines as diverse as Electronic & Electrical Engineering, Marketing, Human Resource Management and Biomedical Enginering, among others.

Explore Strathclyde's Open Access research on smartphone technology now...

Identification of functional connections in biological neural networks using dynamical Bayesian networks

Dong, Chaoyi and Yue, Hong (2016) Identification of functional connections in biological neural networks using dynamical Bayesian networks. IFAC-PapersOnLine. p. 1. ISSN 1474-6670

[img]
Preview
Text (Dong-Yue-IFACO2016-Identification-of-functional-connections-in-biological-neural-networks)
Dong_Yue_IFACO2016_Identification_of_functional_connections_in_biological_neural_networks.pdf - Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo

Download (340kB) | Preview

Abstract

Investigation of the underlying structural characteristics and network properties of biological networks is crucial to understanding the system-level regulatory mechanism of network behaviors. A Dynamic Bayesian Network (DBN) identification method is developed based on the Minimum Description Length (MDL) to identify and locate functional connections among Pulsed Neural Networks (PNN), which are typical in synthetic biological networks. A score of MDL is evaluated for each candidate network structure which includes two factors: i) the complexity of the network; and ii) the likelihood of the network structure based on network dynamic response data. These two factors are combined together to determine the network structure. The DBN is then used to analyze the time-series data from the PNNs, thereby discerning causal connections which collectively show the network structures. Numerical studies on PNN with different number of nodes illustrate the effectiveness of the proposed strategy in network structure identification.